Optimizing the debranning of wheat for incorporation in animal feed production
Bibliographic record
Abstract
Wheat, the predominant feedstock for ethanol production in Canada may be debranned prior to the milling, fermentation and downstream processes. Experiments were carried out using two the Satake mill and tangential abrasive dehulling device (TADD) to remove the bran layer of wheat to optimize the debranning process. Two hundred gram samples of wheat grains were debranned in the Satake mill at grit sizes of 30, 36 and 40, retention time of 30, 60 and 90 s and rotational speed of 1215, 1412 and 1515 rpm or in the TADD at grit sizes of 30, 36, 50 and 80, retention time of 2, 3, 4 and 5 min and rotational speed of 900 rpm. The statistical analysis indicated that rotational speed and retention time were the most significant factors in Satake mill and grit size and retention time affected debranning efficiency in TADD. Using abrasive rollers of higher grit size (fine grit) resulted in a decrease in the percentage removal of bran whereas long retention time caused a high amount of bran to be removed. This, in turn, results in an undesirable loss in starch content of the kernel. . The results indicate that rotation speed of 1412 rpm, 40 grit size and 60 s retention time are the optimum condition for bran production for the Satake mill. Similarly, 900 rpm rotation speed, 50 grit size and 300 s retention time are the optimum conditions for the TADD mill though bran obtained from debranning in TADD was low. Based upon starch separation efficiency, the optimized conditions for the Satake was more desirable compared to TADD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".